some thought on choosing algorithms, algorithm libraries
linear algebra
high level that are easy to write and read: inv, pinv, solve
low level that can be numerically more efficient and finetune results reuse: SVD, QR, ...
sparse versus loops versus pandas groupby
What's the best approach for block structures, group handling and strata
examples:
kronecker product in system of equations,
balanced or unbalanced panel or cluster data
nearest neighbor, matching
still pretty unused: direct interval calculation for 1-D, kdtree, ball point
some thought on choosing algorithms, algorithm libraries
linear algebra
sparse versus loops versus pandas groupby
What's the best approach for block structures, group handling and strata examples: kronecker product in system of equations, balanced or unbalanced panel or cluster data
nearest neighbor, matching
still pretty unused: direct interval calculation for 1-D, kdtree, ball point
examples: local regression (lowess, KernelRegression) matching algorithms: multiple imputation, propensity score matching